import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import plotly.express as px
Orders = pd.read_excel('retail_orders_W23.xlsx')
Store = pd.read_excel('store.xlsx')
Retail_Data = pd.read_csv('retail_data_W23 - retail_data_W23.csv')
sns.heatmap(
Orders.corr(),
annot= True
)
plt.show()
sns.heatmap(
Store.corr(),
annot= True
)
plt.show()
Store.drop(columns=['Promo2'], axis=1, inplace= True)
sns.heatmap(
Store.corr(),
annot= True
)
plt.show()
sns.heatmap(
Retail_Data.corr(),
annot= True
)
plt.show()
avg_customers_by_store = Retail_Data.groupby('Store')['Customers'].mean().reset_index()
fig = px.scatter(avg_customers_by_store, x='Store', y='Customers',
hover_data=['Store', 'Customers'], title='Average Customers per Store')
fig.show()
Retail_Data['Date'] = pd.to_datetime(Retail_Data['Date'])
fig = px.line(Retail_Data, x='Date', y='Customers', title='Daily Total Customers over Time')
fig.show()